Abstract: We are entering an era in quantum computing where the machines being made by Google, IBM, Rigetti, and the like will be able to perform specific useless tasks that classical computers cannot. However, these devices are far from being able to break RSA encryption or simulate complex chemical reactions. A question driving the field is: can we find a practical use for these, so-called, “near-term intermediate scale quantum” (NISQ) devices? A promising approach is to explore “variational quantum algorithms”, which treat a quantum circuit much like an artificial neural network to solve optimization problems approximately. These optimization problems include estimating the ground state energy of a small molecule and dissecting the structure of social networks.

Despite a number of high-profile experimental demonstrations, variational quantum algorithms have yet to outperform state-of-the-art classical optimization techniques. While improving quantum devices is necessary to achieve this so-called “quantum advantage”, improving quantum algorithms also brings us closer towards this goal. This talk will begin with an overview of the state-of-the-art in variational quantum algorithms, highlighting opportunities for improvement. We then focus on one such improvement, which is based on the quantum marginal data extracted from the quantum computer in running a variational quantum algorithm. We describe the marginals optimization procedure (MOP) for improving variational quantum algorithms and demonstrate numerical simulations as well as proof-of-principle experiments using the Rigetti 8-qubit quantum computer. MOP, and similar algorithmic methods, help to bring the utility horizon of quantum computing closer to the present.

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